What Are Dynamic Optimization Problems?

Dynamic Optimization Problems (DOPs) have been widely studied using Evolutionary Algorithms (EAs). Yet, a clear and rigorous definition of DOPs is lacking in the Evolutionary Dynamic Optimization (EDO) community. In this paper, we propose a unified definition of DOPs based on the idea of multiple-decision-making discussed in the Reinforcement Learning (RL) community. We draw a connection between EDO and RL by arguing that both of them are studying DOPs according to our definition of DOPs. We point out that existing EDO or RL research has been mainly focused on some types of DOPs. A conceptualized benchmark problem, which is aimed at the systematic study of various DOPs, is then developed. Some interesting experimental studies on the benchmark reveal that EDO and RL methods are specialized in certain types of DOPs and more importantly new algorithms for DOPs can be developed by combining the strength of both EDO and RL methods.

The com­puter revolu­tion is a revolu­tion in the way we think and in the way we express what we think. The essence of this change is the emer­gence of what might best be called pro­ced­ural epi­stem­o­logy — the study of the struc­ture of know­ledge from an imper­at­ive point of view, as opposed to the more declar­at­ive point of view taken by clas­sical math­em­at­ical sub­jects. Math­em­at­ics provides a frame­work for deal­ing pre­cisely with notions of “what is.” Com­pu­ta­tion provides a frame­work for deal­ing pre­cisely with notions of “how to.”